Open AccessResearch on Simulation and State Prediction of Nuclear Power System Based on LSTM Neural Network
Author(s) -
Yu-Sheng Chen,
Lin Meng,
Ren Yu,
Tianshu Wang
Publication year - 2021
Publication title -
science and technology of nuclear installations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.417
H-Index - 24
eISSN - 1687-6083
pISSN - 1687-6075
DOI - 10.1155/2021/8839867
Subject(s) - key (lock) , nuclear power plant , artificial neural network , nuclear power , electric power system , fault (geology) , computer science , alarm , artificial intelligence , state (computer science) , power (physics) , machine learning , reliability engineering , engineering , control engineering , algorithm , electrical engineering , ecology , physics , computer security , quantum mechanics , seismology , nuclear physics , biology , geology
The nuclear power plant systems are coupled with each other, and their operation conditions are changeable and complex. In the case of an operation fault in these systems, there will be a large number of alarm parameters, which can cause humans to be hurt in the accidents under great pressure. Therefore, it is necessary to predict the values of the key parameters of a device system. The prediction of the key parameters’ values can help operators determine the changing trends of system parameters in advance, which can effectively improve system safety. In this paper, a deep learning long short-term memory (LSTM) neural network model is developed to predict the key parameters of a nuclear power plant. The proposed network is verified by simulations and compared with the traditional grey theory. The simulation and comparison results show that the proposed LSTM neural network is effective and accurate in predicting the key parameters of the nuclear power plant.